Log In Sign Up

Simpler Does It: Generating Semantic Labels with Objectness Guidance

by   Md Amirul Islam, et al.

Existing weakly or semi-supervised semantic segmentation methods utilize image or box-level supervision to generate pseudo-labels for weakly labeled images. However, due to the lack of strong supervision, the generated pseudo-labels are often noisy near the object boundaries, which severely impacts the network's ability to learn strong representations. To address this problem, we present a novel framework that generates pseudo-labels for training images, which are then used to train a segmentation model. To generate pseudo-labels, we combine information from: (i) a class agnostic objectness network that learns to recognize object-like regions, and (ii) either image-level or bounding box annotations. We show the efficacy of our approach by demonstrating how the objectness network can naturally be leveraged to generate object-like regions for unseen categories. We then propose an end-to-end multi-task learning strategy, that jointly learns to segment semantics and objectness using the generated pseudo-labels. Extensive experiments demonstrate the high quality of our generated pseudo-labels and effectiveness of the proposed framework in a variety of domains. Our approach achieves better or competitive performance compared to existing weakly-supervised and semi-supervised methods.


page 9

page 10

page 11

page 13

page 14

page 15

page 16

page 17


Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation

We address the problem of weakly-supervised semantic segmentation (WSSS)...

Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation

Semantic segmentation with limited annotations, such as weakly supervise...

Learning to Detect Semantic Boundaries with Image-level Class Labels

This paper presents the first attempt to learn semantic boundary detecti...

ComSearch: Equation Searching with Combinatorial Strategy for Solving Math Word Problems with Weak Supervision

Previous studies have introduced a weakly-supervised paradigm for solvin...

Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation

Pseudo supervision is regarded as the core idea in semi-supervised learn...

Timestamp-Supervised Action Segmentation in the Perspective of Clustering

Video action segmentation aims to slice the video into several action se...

Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervision

Learning dense point-wise semantics from unstructured 3D point clouds wi...